In order to fully understand the performance of the URIR algorithm, we compared the performance of the aforementioned eight different algorithms on three different data sets, and the results are shown in Figure REF . First of all, Figure REF  (a), Figure REF  (d) and Figure REF  (g) display the variation trend of the Precision of different algorithms on three different data sets as K increases, respectively. All the algorithm result curves in Figure REF  (a) show that as K increases, the overall Precision shows a downward trend. Although the URIR algorithm curve and other algorithm curves is getting closer, the performance of URIR is still better than other algorithms on the whole. For example, when K={1, 2, 4, 5, 6, 8, 10}, compared with the best results of state-of-the-art algorithms, URIR increases by 26.9%, 36.9%, 5.5%, 1.7% and 0.0%, respectively. Additionally, compared with the worst case of these algorithms, when K={1, 2, 4, 5, 6, 8, 10}, URIR increased by 147.5%, 190%, 28.9%, 9.6% and 0.9%, respectively. Compared with the average values of other models when K={1, 2, 4, 5, 6, 8, 10}, URIR increased by 61.0%, 64.9%, 14.6%, 4.3% and 0.5%, respectively. Similarly, both the ml and yelp data sets show the same characteristics. This phenomenon is consistent with previous studies [1]}, [2]}, [3]}, [4]}.
